Codes
Tick Chart for MetaTrader 4
The presented indicator plots a fully-functional tick chart similar to the standard price charts, with the ability of the analysis using all the MetaTrader features
Articles
Neural Networks in Trading: A Spatio-Temporal State Space Model for Financial Data Analysis (STSSM Block) for MetaTrader 5
The article explains the internal mechanics of the STSSM block and demonstrates how modern SSM approaches can be adapted to the event-driven logic of spiking models while maintaining high speed and expressive representations. Step by step, we work our way through the architecture, transforming the
Neural Networks in Trading: A Spatio-Temporal State-Space Model for Financial Data Analysis (E-STMFlow) for MetaTrader 5
We invite you to explore the E-STMFlow framework, which effectively processes event streams by extracting informative embeddings, filtering out noise, and identifying key movements. Its architecture makes it possible to identify complex relationships between features and provides scalability
Neural Networks in Trading: A Spiking Architecture for Spatio-Temporal Market Analysis (Conclusion) for MetaTrader 5
The SDformerFlow framework transforms complex events in financial time series into structured representations, enabling the model to identify local fluctuations and global trends simultaneously. The multi-level U-structure ensures consistency between the forward pass and the backward pass
Neural Networks in Trading: A Spiking Architecture for Spatio-Temporal Market Analysis (Encoder) for MetaTrader 5
The article presents an adaptation of the SDformerFlow framework that achieves high adaptability by integrating spiking attention with multi-window convolution and weighted summation of query elements. The architecture allows each attention head to learn its own parameters, which improves the
Neural Networks in Trading: A Spiking Architecture for Spatio-Temporal Market Analysis (SDformerFlow) for MetaTrader 5
The article presents an adaptation of the SDformerFlow spiking architecture for the task of dense analysis of price micro-movements. The spatio-temporal structure provides a high level of detail, while the spiking logic ensures computational efficiency and the ability to work with sparse, pulse-like
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (Conclusion) for MetaTrader 5
The implementation of the EV-MGRFlowNet framework demonstrates its key advantages: modularity, resilience to market fluctuations, and the ability to develop strategies independently. These features make the framework a powerful tool for analysis, forecasting, and the development of autonomous
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (Encoder) for MetaTrader 5
The article takes readers deep into the heart of the EV-MGRFlowNet framework, showing how its architecture is applied in a practical implementation for financial forecasting tasks. Step by step, we build a carefully designed combination of modules capable of detecting subtle temporal patterns and
Neural Networks in Trading: Recurrent Modeling of Market Micromovements (EV-MGRFlowNet) for MetaTrader 5
The article discusses the adaptation of the EV-MGRFlowNet architecture — originally developed for processing event-based video data — to the field of financial time series. The approach presented here offers a new perspective on the market as a stream of micro-movements, where price, volume, and
Neural Networks in Trading: Temporal Motion Aggregation (Conclusion) for MetaTrader 5
Introducing the TMA framework — an intelligent system capable of forecasting market dynamics with sufficient accuracy. In this article, we have integrated all the components into a single architecture and transformed it into a fully-fledged trading agent that analyzes the market and makes decisions
Neural Networks in Trading: Temporal Motion Aggregation (Key Components) for MetaTrader 5
In this article, theory meets practice. We implement the key modules of the TMA framework — MPE and MPA. Here, data takes on meaning, and cross-attention becomes a tool for precise analysis of market dynamics. Minimal unnecessary operations, maximum efficiency — a step toward next-generation